Local Context Compression: Big or Small?

<!-- SC_OFF --><div class="md"><p>Hi. What do you think is the smallest Open model, as of Spring 2026, that can match/beat OpenAI GPT 4o Mini in the broadest sense possible?</p>

<!-- SC_OFF --><div class="md"><p>The news about Ulysses Sequence Parallelism enabling million-token contexts is fascinating for local LLMs. While the potential for deeper context

<!-- SC_OFF --><div class="md"><p>Hey <a href="/r/LocalLLaMA">r/LocalLLaMA</a>,</p> <p>We’re living through something wild: “intelligence density” / capability density is scaling

<!-- SC_OFF --><div class="md"><p>Been building AI agents for a while and kept hitting the same wall: agents that work great at the start of a session and quietly fall apart by

<!-- SC_OFF --><div class="md"><p>We all know about context rotting (loss of model accuracy over long context).<br /> Many times I see some saying &quot;try with 32K context and

<!-- SC_OFF --><div class="md"><p>Happy with Qwen3.8-27B, but that xhigh thinking mode is chewing through context like nobody's business. I'm hoping I can point Hermes at a

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1wlss89/postulate_context_compaction_is_good_for/"> <img alt="Postulate: Context compaction is good for

<!-- SC_OFF --><div class="md"><p>How do you guys deal with long context, for example while coding, when you’re going back and forth for adjustments or fixing some errors and